Environmental concerns have motivated the implementation of emission requirements for internal combustion engines and other combustion systems throughout much of the world. Catalytic converters have been used to eliminate many of the pollutants present in exhaust gas; however, a filter is often required to remove particulate matter, such as, for example, ash and soot. Wall-flow particulate filters, for example, are often used in engine systems to remove particulates from the exhaust gas. Such particulate filters may be made of a honeycomb-like substrate with parallel flow channels or cells separated by internal porous walls. Inlet and outlet ends of the flow channels may be selectively plugged, such as, for example, in a checkerboard pattern, so that exhaust gas, once inside the substrate, is forced to pass through the internal porous walls, whereby the porous walls retain a portion of the particulates in the exhaust gas.
In this manner, wall-flow particulate filters have been found to be effective in removing particulates from exhaust gas. However, the pressure drop across the wall-flow particulate filter increases as the amount of particulates trapped in the porous walls increases. The increasing pressure drop results in a gradual rise in back pressure against the engine, and a corresponding decrease in the performance of the engine. Accordingly, soot is commonly oxidized and removed in a controlled regeneration process before excessive levels have accumulated.
The ability to measure or estimate the amount of particulate, such as, for example, soot accumulated in a particulate filter is valuable as it helps to determine the regeneration schedule for the filter. Optimizing a filter's regeneration frequency, for example, can reduce the negative impacts of regeneration (e.g. increased emissions and fuel consumption) from too frequent regeneration, and protect the filter from over-exposure and possible failure due to the heightened energy release caused by excessive particulate loading from too infrequent regeneration. Accurately estimating the particulate load level (e.g., soot load level) in a particulate filter may thus facilitate determining when to initiate a timely and controlled regeneration event.
Conventional methodologies for estimating soot load in a particulate filter include both pressure drop based techniques and mass balance based techniques. A benefit of a pressure drop method is the closed loop feedback that it provides; however, the accuracy in the soot load predictability of conventional pressure drop approaches is still quite limited. Common pressure drop based techniques include, for example, using a differential pressure sensor to measure the pressure change of exhaust gas upstream and downstream from a particulate filter, and approximating a soot load based on the pressure change. Such approaches, however, may not account for a particulate filter's total pressure drop behavior (e.g. including pressure drop contributions from a filter's inlet/outlet losses, channel losses and permeable layer losses) and as a result some approaches rely on an empirical correlation in order to estimate soot load from the differential pressure sensor response. The reliance on empirical correlations may be relatively inaccurate over wide temperature and flow ranges, particularly performing poorly under dynamic conditions. Furthermore, such approaches do not account for the impact of ash loading in the filter or the impact of non-continuum gas effects on the pressure drop behavior of the filter.
An approach to estimating soot load that does not account for a filter's total pressure drop behavior, including contributions from inlet contraction and outlet expansion losses as fluid flows through the channels of a particulate filter, has limited accuracy. For example, such an approach would be accurate only under certain conditions, for example, at large soot load levels when channel and permeable layer losses substantially dominate inlet/outlet losses.
In one relatively recent pressure drop based approach, non-continuum gas effects on the pressure drop behavior of a particulate filter are considered based on empirical data fitting, where the cake soot layer permeability has been decoupled into temperature and mass flow rate contributions. Such an empirical approach, however, can provide only derived constants that would need reevaluation for any change in filter geometry and/or microstructure, thereby making such an approach applicable over only a relatively narrow range of conditions and filter configurations.
A need still exists, therefore, for a pressure drop based approach with a high level of accuracy over a wide range of operating conditions and for a wide range of filter geometries and microstructure characteristics.